Session 4: Homework 2
Study Group A13 - Philippine Bonnaud, Ieuan Edmonds, Maoze Li, Yuzhou Li, Francesco Sartori and Sanskriti Bajaj
2021-10-03
1 Climate change and temperature anomalies
If we wanted to study climate change, we can find data on the Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies in the Northern Hemisphere at NASA’s Goddard Institute for Space Studies. The tabular data of temperature anomalies can be found here
To define temperature anomalies you need to have a reference, or base, period which NASA clearly states that it is the period between 1951-1980.
Run the code below to load the file:
weather <-
read_csv("https://data.giss.nasa.gov/gistemp/tabledata_v4/NH.Ts+dSST.csv",
skip = 1,
na = "***")Notice that, when using this function, we added two options: skip and na.
- The
skip=1option is there as the real data table only starts in Row 2, so we need to skip one row. na = "***"option informs R how missing observations in the spreadsheet are coded. When looking at the spreadsheet, you can see that missing data is coded as "***". It is best to specify this here, as otherwise some of the data is not recognized as numeric data.
Once the data is loaded, notice that there is a object titled weather in the Environment panel. If you cannot see the panel (usually on the top-right), go to Tools > Global Options > Pane Layout and tick the checkbox next to Environment. Click on the weather object, and the dataframe will pop up on a seperate tab. Inspect the dataframe.
For each month and year, the dataframe shows the deviation of temperature from the normal (expected). Further the dataframe is in wide format.
You have two objectives in this section:
Select the year and the twelve month variables from the
weatherdataset. We do not need the others (J-D, D-N, DJF, etc.) for this assignment. Hint: useselect()function.Convert the dataframe from wide to ‘long’ format. Hint: use
gather()orpivot_longer()function. Name the new dataframe astidyweather, name the variable containing the name of the month asmonth, and the temperature deviation values asdelta.
glimpse(weather)## Rows: 142
## Columns: 19
## $ Year <dbl> 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890…
## $ Jan <dbl> -0.34, -0.30, 0.27, -0.57, -0.16, -1.00, -0.74, -1.08, -0.49, -0…
## $ Feb <dbl> -0.50, -0.21, 0.22, -0.65, -0.10, -0.44, -0.83, -0.70, -0.61, 0.…
## $ Mar <dbl> -0.22, -0.03, 0.02, -0.15, -0.64, -0.23, -0.72, -0.44, -0.64, -0…
## $ Apr <dbl> -0.29, 0.01, -0.31, -0.30, -0.59, -0.48, -0.37, -0.38, -0.22, 0.…
## $ May <dbl> -0.05, 0.04, -0.24, -0.25, -0.35, -0.58, -0.33, -0.25, -0.15, -0…
## $ Jun <dbl> -0.15, -0.32, -0.29, -0.11, -0.41, -0.44, -0.37, -0.20, -0.03, -…
## $ Jul <dbl> -0.17, 0.09, -0.27, -0.05, -0.44, -0.34, -0.15, -0.24, -0.01, -0…
## $ Aug <dbl> -0.25, -0.03, -0.14, -0.22, -0.50, -0.41, -0.43, -0.54, -0.21, -…
## $ Sep <dbl> -0.22, -0.25, -0.24, -0.33, -0.44, -0.40, -0.33, -0.21, -0.20, -…
## $ Oct <dbl> -0.31, -0.42, -0.52, -0.16, -0.44, -0.37, -0.31, -0.49, -0.04, -…
## $ Nov <dbl> -0.42, -0.36, -0.32, -0.44, -0.57, -0.38, -0.39, -0.27, -0.01, -…
## $ Dec <dbl> -0.39, -0.22, -0.68, -0.14, -0.46, -0.11, -0.22, -0.43, -0.24, -…
## $ `J-D` <dbl> -0.28, -0.17, -0.21, -0.28, -0.43, -0.43, -0.43, -0.44, -0.24, -…
## $ `D-N` <dbl> NA, -0.18, -0.17, -0.33, -0.40, -0.46, -0.42, -0.42, -0.25, -0.1…
## $ DJF <dbl> NA, -0.30, 0.09, -0.63, -0.14, -0.64, -0.56, -0.66, -0.51, -0.08…
## $ MAM <dbl> -0.19, 0.01, -0.17, -0.23, -0.53, -0.43, -0.47, -0.36, -0.34, 0.…
## $ JJA <dbl> -0.19, -0.09, -0.23, -0.12, -0.45, -0.40, -0.32, -0.33, -0.08, -…
## $ SON <dbl> -0.32, -0.34, -0.36, -0.31, -0.48, -0.38, -0.35, -0.32, -0.08, -…which(is.na(weather))## [1] 1420 1562 1704 1846 1988 1989 2130 2131 2698tidyweather <- weather %>% #selecting the necessary variables
select(Year,Jan, Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec)%>%
pivot_longer(cols=c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"), names_to = "month",values_to = "delta") #using pivot longer to convert the dataframe from wide to long
tidyweather## # A tibble: 1,704 × 3
## Year month delta
## <dbl> <chr> <dbl>
## 1 1880 Jan -0.34
## 2 1880 Feb -0.5
## 3 1880 Mar -0.22
## 4 1880 Apr -0.29
## 5 1880 May -0.05
## 6 1880 Jun -0.15
## 7 1880 Jul -0.17
## 8 1880 Aug -0.25
## 9 1880 Sep -0.22
## 10 1880 Oct -0.31
## # … with 1,694 more rowsInspect your dataframe. It should have three variables now, one each for
- year,
- month, and
- delta, or temperature deviation.
1.1 Plotting Information
Let us plot the data using a time-series scatter plot, and add a trendline. To do that, we first need to create a new variable called date in order to ensure that the delta values are plot chronologically.
In the following chunk of code, I used the
eval=FALSEargument, which does not run a chunk of code; I did so that you can knit the document before tidying the data and creating a new dataframetidyweather. When you actually want to run this code and knit your document, you must deleteeval=FALSE, not just here but in all chunks wereeval=FALSEappears.
tidyweather <- tidyweather %>% #selecting the datav
mutate(date = ymd(paste(as.character(Year), month, "1")), #adding a new variable called "date" to the dataframe
month = month(date, label=TRUE),
year = year(date))
ggplot(tidyweather, aes(x=date, y = delta))+ #plotting delta chronologically
geom_point()+
geom_smooth(color="red") +
theme_bw() +
labs (
title = "Weather Anomalies"
)
Is the effect of increasing temperature more pronounced in some months? Use facet_wrap() to produce a seperate scatter plot for each month, again with a smoothing line. Your chart should human-readable labels; that is, each month should be labeled “Jan”, “Feb”, “Mar” (full or abbreviated month names are fine), not 1, 2, 3.
It is sometimes useful to group data into different time periods to study historical data. For example, we often refer to decades such as 1970s, 1980s, 1990s etc. to refer to a period of time. NASA calcuialtes a temperature anomaly, as difference form the base periof of 1951-1980. The code below creates a new data frame called comparison that groups data in five time periods: 1881-1920, 1921-1950, 1951-1980, 1981-2010 and 2011-present.
We remove data before 1800 and before using filter. Then, we use the mutate function to create a new variable interval which contains information on which period each observation belongs to. We can assign the different periods using case_when().
comparison <- tidyweather %>%
filter(Year>= 1881) %>% #remove years prior to 1881
#create new variable 'interval', and assign values based on criteria below:
mutate(interval = case_when(
Year %in% c(1881:1920) ~ "1881-1920",
Year %in% c(1921:1950) ~ "1921-1950",
Year %in% c(1951:1980) ~ "1951-1980",
Year %in% c(1981:2010) ~ "1981-2010",
TRUE ~ "2011-present"
))Inspect the comparison dataframe by clicking on it in the Environment pane.
Now that we have the interval variable, we can create a density plot to study the distribution of monthly deviations (delta), grouped by the different time periods we are interested in. Set fill to interval to group and colour the data by different time periods.
ggplot(comparison, aes(x=delta, fill=interval))+
geom_density(alpha=0.2) + #density plot with tranparency set to 20%
theme_bw() + #theme
labs (
title = "Density Plot for Monthly Temperature Anomalies",
y = "Density" #changing y-axis label to sentence case
)So far, we have been working with monthly anomalies. However, we might be interested in average annual anomalies. We can do this by using group_by() and summarise(), followed by a scatter plot to display the result.
#creating yearly averages
average_annual_anomaly <- tidyweather %>%
group_by(Year) %>% #grouping data by Year
# creating summaries for mean delta
# use `na.rm=TRUE` to eliminate NA (not available) values
summarise(annual_average_delta=mean(delta))
#plotting the data:
ggplot(average_annual_anomaly, aes(x=Year, y= annual_average_delta))+
geom_point()+
#Fit the best fit line, using LOESS method
geom_smooth() +
#change to theme_bw() to have white background + black frame around plot
theme_bw() +
labs (
title = "Average Yearly Anomaly",
y = "Average Annual Delta"
) 
1.2 Confidence Interval for delta
NASA points out on their website that
A one-degree global change is significant because it takes a vast amount of heat to warm all the oceans, atmosphere, and land by that much. In the past, a one- to two-degree drop was all it took to plunge the Earth into the Little Ice Age.
Your task is to construct a confidence interval for the average annual delta since 2011, both using a formula and using a bootstrap simulation with the infer package. Recall that the dataframe comparison has already grouped temperature anomalies according to time intervals; we are only interested in what is happening between 2011-present.
comparison <- na.omit(comparison) #to eliminate the N/A starting from September 2021
comparison## # A tibble: 1,688 × 6
## Year month delta date year interval
## <dbl> <ord> <dbl> <date> <dbl> <chr>
## 1 1881 Jan -0.3 1881-01-01 1881 1881-1920
## 2 1881 Feb -0.21 1881-02-01 1881 1881-1920
## 3 1881 Mar -0.03 1881-03-01 1881 1881-1920
## 4 1881 Apr 0.01 1881-04-01 1881 1881-1920
## 5 1881 May 0.04 1881-05-01 1881 1881-1920
## 6 1881 Jun -0.32 1881-06-01 1881 1881-1920
## 7 1881 Jul 0.09 1881-07-01 1881 1881-1920
## 8 1881 Aug -0.03 1881-08-01 1881 1881-1920
## 9 1881 Sep -0.25 1881-09-01 1881 1881-1920
## 10 1881 Oct -0.42 1881-10-01 1881 1881-1920
## # … with 1,678 more rowsformula_ci <- comparison %>%
filter(interval=="2011-present")%>% # choose the interval 2011-present
summarise(mean_delta=mean(delta), #using summarise
sd_delta=sd(delta),
count=n(),
t_critical=qt(0.975,count-1),
se_delta=sd(delta)/sqrt(count),
margin_of_error=t_critical*se_delta,
delta_low=mean_delta-margin_of_error,
delta_high=mean_delta+margin_of_error)%>%
arrange(desc(mean_delta))
#print out formula_CI
formula_ci## # A tibble: 1 × 8
## mean_delta sd_delta count t_critical se_delta margin_of_error delta_low
## <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 1.06 0.274 128 1.98 0.0243 0.0480 1.01
## # … with 1 more variable: delta_high <dbl># use the infer package to construct a 95% CI for delta
boot_ratings <- comparison %>%
filter(interval == "2011-present") %>%
specify(response = delta) %>%
generate(reps = 1000, type = "bootstrap") %>%
calculate(stat = "mean")
percentile_ci <- boot_ratings %>%
get_confidence_interval(level = 0.95, type = "percentile")
percentile_ci## # A tibble: 1 × 2
## lower_ci upper_ci
## <dbl> <dbl>
## 1 1.01 1.11What is the data showing us? Please type your answer after (and outside!) this blockquote. You have to explain what you have done, and the interpretation of the result. One paragraph max, please!
The data highlights that there is a 95% probability that the temperature will deviate from the avergae and that the deviation will lie between 1.01 to 1.1, This highlights that there has been rise in temperature and the severity of the rise can differ.
2 Global warming and political views (GSS)
A 2010 Pew Research poll asked 1,306 Americans, “From what you’ve read and heard, is there solid evidence that the average temperature on earth has been getting warmer over the past few decades, or not?”
In this exercise we analyze whether there are any differences between the proportion of people who believe the earth is getting warmer and their political ideology. As usual, from the survey sample data, we will use the proportions to estimate values of population parameters. The file has 2253 observations on the following 2 variables:
party_or_ideology: a factor (categorical) variable with levels Conservative Republican, Liberal Democrat, Mod/Cons Democrat, Mod/Lib Republicanresponse: whether the respondent believes the earth is warming or not, or Don’t know/ refuse to answer
global_warming_pew <- read_csv(here::here("data", "global_warming_pew.csv"))You will also notice that many responses should not be taken into consideration, like “No Answer”, “Don’t Know”, “Not applicable”, “Refused to Answer”.
global_warming_pew %>% #analyzing the number of responses that need to be omitted
count(party_or_ideology, response)## # A tibble: 12 × 3
## party_or_ideology response n
## <chr> <chr> <int>
## 1 Conservative Republican Don't know / refuse to answer 45
## 2 Conservative Republican Earth is warming 248
## 3 Conservative Republican Not warming 450
## 4 Liberal Democrat Don't know / refuse to answer 23
## 5 Liberal Democrat Earth is warming 405
## 6 Liberal Democrat Not warming 23
## 7 Mod/Cons Democrat Don't know / refuse to answer 45
## 8 Mod/Cons Democrat Earth is warming 563
## 9 Mod/Cons Democrat Not warming 158
## 10 Mod/Lib Republican Don't know / refuse to answer 23
## 11 Mod/Lib Republican Earth is warming 135
## 12 Mod/Lib Republican Not warming 135We will be constructing three 95% confidence intervals to estimate population parameters, for the % who believe that Earth is warming, according to their party or ideology. You can create the CIs using the formulas by hand, or use prop.test()– just remember to exclude the Dont know / refuse to answer!
Does it appear that whether or not a respondent believes the earth is warming is independent of their party ideology?
Yes from the data it can be inferred that a respondents view about increase in global temperatures can be skewed as per the political and party ideology they share. This is evident as more than 50% of Conservative Republicans have voted that they don’t believe that temperatures are rising. On the other hand more than 50% of Liberal Democrats have voted that they believe that temperatures are rising. Democrats in each category (liberal, moderate or conservative) have a higher propensity to vote yes to rise in temperatures, whereas the opposite is true for Republicans. This highlights that an individual’s views about rising prices are driven by the political camp they are associated with i.e. Republican or Democratic rather than the degree of their affiliation i.e. liberal, moderate or conservative.
You may want to read on The challenging politics of climate change
global_warming2 <- global_warming_pew[!(global_warming_pew$response == "Don't know / refuse to answer"),] #removing the unwanted responses from the dataset
global_warming2## # A tibble: 2,117 × 2
## party_or_ideology response
## <chr> <chr>
## 1 Conservative Republican Earth is warming
## 2 Conservative Republican Earth is warming
## 3 Conservative Republican Earth is warming
## 4 Conservative Republican Earth is warming
## 5 Conservative Republican Earth is warming
## 6 Conservative Republican Earth is warming
## 7 Conservative Republican Earth is warming
## 8 Conservative Republican Earth is warming
## 9 Conservative Republican Earth is warming
## 10 Conservative Republican Earth is warming
## # … with 2,107 more rowsglobal_warming2 %>%
count(party_or_ideology, response) #counting by repsonse type to ensure that the unwanted responses have been omitted## # A tibble: 8 × 3
## party_or_ideology response n
## <chr> <chr> <int>
## 1 Conservative Republican Earth is warming 248
## 2 Conservative Republican Not warming 450
## 3 Liberal Democrat Earth is warming 405
## 4 Liberal Democrat Not warming 23
## 5 Mod/Cons Democrat Earth is warming 563
## 6 Mod/Cons Democrat Not warming 158
## 7 Mod/Lib Republican Earth is warming 135
## 8 Mod/Lib Republican Not warming 135prop.test(248,698)##
## 1-sample proportions test with continuity correction
##
## data: 248 out of 698
## X-squared = 58, df = 1, p-value = 3e-14
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.320 0.392
## sample estimates:
## p
## 0.355prop.test(405,428)##
## 1-sample proportions test with continuity correction
##
## data: 405 out of 428
## X-squared = 339, df = 1, p-value <2e-16
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.919 0.965
## sample estimates:
## p
## 0.946prop.test(563,721)##
## 1-sample proportions test with continuity correction
##
## data: 563 out of 721
## X-squared = 226, df = 1, p-value <2e-16
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.748 0.810
## sample estimates:
## p
## 0.781prop.test(135,270)##
## 1-sample proportions test without continuity correction
##
## data: 135 out of 270
## X-squared = 0, df = 1, p-value = 1
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.441 0.559
## sample estimates:
## p
## 0.53 Biden’s Approval Margins
As we saw in class, fivethirtyeight.com has detailed data on all polls that track the president’s approval
# Import approval polls data directly off fivethirtyeight website
approval_pollist <- read_csv('https://projects.fivethirtyeight.com/biden-approval-data/approval_polllist.csv')
glimpse(approval_pollist)## Rows: 1,819
## Columns: 22
## $ president <chr> "Joseph R. Biden Jr.", "Joseph R. Biden Jr.", "Jos…
## $ subgroup <chr> "All polls", "All polls", "All polls", "All polls"…
## $ modeldate <chr> "10/1/2021", "10/1/2021", "10/1/2021", "10/1/2021"…
## $ startdate <chr> "1/19/2021", "1/19/2021", "1/20/2021", "1/20/2021"…
## $ enddate <chr> "1/21/2021", "1/21/2021", "1/21/2021", "1/21/2021"…
## $ pollster <chr> "Rasmussen Reports/Pulse Opinion Research", "Morni…
## $ grade <chr> "B", "B", "B+", "B", "B-", "B", "B+", "B-", "B", "…
## $ samplesize <dbl> 1500, 15000, 1516, 1993, 1115, 15000, 941, 1200, 1…
## $ population <chr> "lv", "a", "a", "rv", "a", "a", "rv", "rv", "lv", …
## $ weight <dbl> 0.3382, 0.2594, 1.2454, 0.0930, 1.1014, 0.2333, 1.…
## $ influence <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ approve <dbl> 48, 50, 45, 56, 55, 51, 63, 58, 48, 52, 53, 55, 48…
## $ disapprove <dbl> 45, 28, 28, 31, 32, 28, 37, 32, 47, 29, 29, 33, 47…
## $ adjusted_approve <dbl> 50.5, 48.6, 46.5, 54.6, 53.9, 49.6, 58.7, 56.9, 50…
## $ adjusted_disapprove <dbl> 38.8, 31.3, 28.4, 34.3, 32.9, 31.3, 38.0, 33.1, 40…
## $ multiversions <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tracking <lgl> TRUE, TRUE, NA, NA, NA, TRUE, NA, NA, TRUE, TRUE, …
## $ url <chr> "https://www.rasmussenreports.com/public_content/p…
## $ poll_id <dbl> 74247, 74272, 74327, 74246, 74248, 74273, 74256, 7…
## $ question_id <dbl> 139395, 139491, 139570, 139394, 139404, 139492, 13…
## $ createddate <chr> "1/22/2021", "1/28/2021", "2/2/2021", "1/22/2021",…
## $ timestamp <chr> "10:23:09 1 Oct 2021", "10:23:09 1 Oct 2021", "1…str(approval_pollist)## spec_tbl_df [1,819 × 22] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ president : chr [1:1819] "Joseph R. Biden Jr." "Joseph R. Biden Jr." "Joseph R. Biden Jr." "Joseph R. Biden Jr." ...
## $ subgroup : chr [1:1819] "All polls" "All polls" "All polls" "All polls" ...
## $ modeldate : chr [1:1819] "10/1/2021" "10/1/2021" "10/1/2021" "10/1/2021" ...
## $ startdate : chr [1:1819] "1/19/2021" "1/19/2021" "1/20/2021" "1/20/2021" ...
## $ enddate : chr [1:1819] "1/21/2021" "1/21/2021" "1/21/2021" "1/21/2021" ...
## $ pollster : chr [1:1819] "Rasmussen Reports/Pulse Opinion Research" "Morning Consult" "YouGov" "Morning Consult" ...
## $ grade : chr [1:1819] "B" "B" "B+" "B" ...
## $ samplesize : num [1:1819] 1500 15000 1516 1993 1115 ...
## $ population : chr [1:1819] "lv" "a" "a" "rv" ...
## $ weight : num [1:1819] 0.338 0.259 1.245 0.093 1.101 ...
## $ influence : num [1:1819] 0 0 0 0 0 0 0 0 0 0 ...
## $ approve : num [1:1819] 48 50 45 56 55 51 63 58 48 52 ...
## $ disapprove : num [1:1819] 45 28 28 31 32 28 37 32 47 29 ...
## $ adjusted_approve : num [1:1819] 50.5 48.6 46.5 54.6 53.9 ...
## $ adjusted_disapprove: num [1:1819] 38.8 31.3 28.4 34.3 32.9 ...
## $ multiversions : chr [1:1819] NA NA NA NA ...
## $ tracking : logi [1:1819] TRUE TRUE NA NA NA TRUE ...
## $ url : chr [1:1819] "https://www.rasmussenreports.com/public_content/politics/biden_administration/biden_approval_index_history" "https://morningconsult.com/form/global-leader-approval/" "https://docs.cdn.yougov.com/u3h9dresbn/20210120_yahoo_coronavirus_toplines.pdf" "https://assets.morningconsult.com/wp-uploads/2021/01/21155806/210166_crosstabs_MC_WASHINGTON_RVs_v2.pdf" ...
## $ poll_id : num [1:1819] 74247 74272 74327 74246 74248 ...
## $ question_id : num [1:1819] 139395 139491 139570 139394 139404 ...
## $ createddate : chr [1:1819] "1/22/2021" "1/28/2021" "2/2/2021" "1/22/2021" ...
## $ timestamp : chr [1:1819] "10:23:09 1 Oct 2021" "10:23:09 1 Oct 2021" "10:23:09 1 Oct 2021" "10:23:09 1 Oct 2021" ...
## - attr(*, "spec")=
## .. cols(
## .. president = col_character(),
## .. subgroup = col_character(),
## .. modeldate = col_character(),
## .. startdate = col_character(),
## .. enddate = col_character(),
## .. pollster = col_character(),
## .. grade = col_character(),
## .. samplesize = col_double(),
## .. population = col_character(),
## .. weight = col_double(),
## .. influence = col_double(),
## .. approve = col_double(),
## .. disapprove = col_double(),
## .. adjusted_approve = col_double(),
## .. adjusted_disapprove = col_double(),
## .. multiversions = col_character(),
## .. tracking = col_logical(),
## .. url = col_character(),
## .. poll_id = col_double(),
## .. question_id = col_double(),
## .. createddate = col_character(),
## .. timestamp = col_character()
## .. )
## - attr(*, "problems")=<externalptr># Use `lubridate` to fix dates, as they are given as characters.approval_pollist1 <- approval_pollist%>% #converting the date from character to date
mutate(enddate2 = as.Date(approval_pollist$enddate, format = "%m/%d/%Y"),
week = isoweek(enddate2),
net_approval_rate = approve-disapprove)
approval_pollist1 ## # A tibble: 1,819 × 25
## president subgroup modeldate startdate enddate pollster grade samplesize
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 Joseph R. … All pol… 10/1/2021 1/19/2021 1/21/2… Rasmussen … B 1500
## 2 Joseph R. … All pol… 10/1/2021 1/19/2021 1/21/2… Morning Co… B 15000
## 3 Joseph R. … All pol… 10/1/2021 1/20/2021 1/21/2… YouGov B+ 1516
## 4 Joseph R. … All pol… 10/1/2021 1/20/2021 1/21/2… Morning Co… B 1993
## 5 Joseph R. … All pol… 10/1/2021 1/20/2021 1/21/2… Ipsos B- 1115
## 6 Joseph R. … All pol… 10/1/2021 1/20/2021 1/22/2… Morning Co… B 15000
## 7 Joseph R. … All pol… 10/1/2021 1/21/2021 1/22/2… HarrisX B+ 941
## 8 Joseph R. … All pol… 10/1/2021 1/21/2021 1/23/2… RMG Resear… B- 1200
## 9 Joseph R. … All pol… 10/1/2021 1/20/2021 1/24/2… Rasmussen … B 1500
## 10 Joseph R. … All pol… 10/1/2021 1/21/2021 1/23/2… Morning Co… B 15000
## # … with 1,809 more rows, and 17 more variables: population <chr>,
## # weight <dbl>, influence <dbl>, approve <dbl>, disapprove <dbl>,
## # adjusted_approve <dbl>, adjusted_disapprove <dbl>, multiversions <chr>,
## # tracking <lgl>, url <chr>, poll_id <dbl>, question_id <dbl>,
## # createddate <chr>, timestamp <chr>, enddate2 <date>, week <dbl>,
## # net_approval_rate <dbl>approval_ci <- approval_pollist1%>% #creating a CI for approval rating on the end date
group_by(week)%>%
summarise(mean_rate=mean(net_approval_rate),
sd_rate=sd(net_approval_rate),
count=n(),
t_critical=qt(0.95,count-1),
se_rate=sd(net_approval_rate)/sqrt(count),
margin_of_error=t_critical*se_rate,
rate_low=mean_rate-margin_of_error,
rate_high=mean_rate+margin_of_error)%>%
arrange(desc(mean_rate))
approval_ci %>%
arrange(week)%>%
select(week, mean_rate, rate_low, rate_high)## # A tibble: 37 × 4
## week mean_rate rate_low rate_high
## <dbl> <dbl> <dbl> <dbl>
## 1 3 20.2 17.7 22.8
## 2 4 18.1 16.1 20.1
## 3 5 16.2 14.3 18.1
## 4 6 16.7 14.7 18.6
## 5 7 16.1 14.4 17.9
## 6 8 13.8 12.1 15.5
## 7 9 12.9 11.1 14.7
## 8 10 14.3 12.6 16.1
## 9 11 16.1 13.9 18.2
## 10 12 14.0 12.1 15.9
## # … with 27 more rows3.1 Create a plot
What I would like you to do is to calculate the average net approval rate (approve- disapprove) for each week since he got into office. I want you plot the net approval, along with its 95% confidence interval. There are various dates given for each poll, please use enddate, i.e., the date the poll ended.
ggplot(approval_ci, aes(x= week, y= mean_rate))+ #plotting the net approval rate along with the CI
geom_line(colour="red", size=1/10)+
geom_point(colour="red", size= 1)+
geom_smooth(colour="blue")+
geom_ribbon(aes(ymin=rate_low, ymax=rate_high), alpha = 0.1, colour="red")+
geom_hline(yintercept=0, colour="orange", size=1)+
labs(title = "Estimating Approval Margin(approve-disapprove) for Joe Biden",
subtitle = "Weekly average of all polls",
y = "Average Approval Margin(approve-disapprove)",
x = "Week of the year")+
facet_wrap(2021, scales = "free")+
theme_minimal()
Also, please add an orange line at zero. Your plot should look like this:
3.2 Compare Confidence Intervals
Compare the confidence intervals for week 3 and week 25. Can you explain what’s going on? One paragraph would be enough. Mean and median rates decreased significantly from week 3 to week 25, with average approval rate decreased by almost 50%. Additionally, the confidence interval becomes slimmer (with lower SD and SE) due to the increased sample size over the whole polling period (count in week 3 and 25 is 28 and 43 respectively). That could imply that there might be a slight negative bias, as people tend to express their dissatisfaction stronger than their satisfaction, resulting in more people engaging in the polls when they are unsatisfied. A more reliable sample would have a more constant count of participants over the whole period (count almost doubles from week 3 to 25).
4 Challenge 1: Excess rentals in TfL bike sharing
Recall the TfL data on how many bikes were hired every single day. We can get the latest data by running the following
url <- "https://data.london.gov.uk/download/number-bicycle-hires/ac29363e-e0cb-47cc-a97a-e216d900a6b0/tfl-daily-cycle-hires.xlsx"
# Download TFL data to temporary file
httr::GET(url, write_disk(bike.temp <- tempfile(fileext = ".xlsx")))## Response [https://airdrive-secure.s3-eu-west-1.amazonaws.com/london/dataset/number-bicycle-hires/2021-09-23T12%3A52%3A20/tfl-daily-cycle-hires.xlsx?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAJJDIMAIVZJDICKHA%2F20211003%2Feu-west-1%2Fs3%2Faws4_request&X-Amz-Date=20211003T150916Z&X-Amz-Expires=300&X-Amz-Signature=9ce6a238812162683a108aaa804de849ddca42770b11fbea925ebe709c2dd0d2&X-Amz-SignedHeaders=host]
## Date: 2021-10-03 15:09
## Status: 200
## Content-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
## Size: 174 kB
## <ON DISK> /var/folders/tx/74gc7yfd1xq27tc2q231vt2w0000gn/T//Rtmpz6qp4P/file83c1604a493.xlsx# Use read_excel to read it as dataframe
bike0 <- read_excel(bike.temp,
sheet = "Data",
range = cell_cols("A:B"))
# change dates to get year, month, and week
bike <- bike0 %>%
clean_names() %>%
rename (bikes_hired = number_of_bicycle_hires) %>%
mutate (year = year(day),
month = lubridate::month(day, label = TRUE),
week = isoweek(day))We can easily create a facet grid that plots bikes hired by month and year.
Look at May and Jun and compare 2020 with the previous years. What’s happening? Due to the COVID pandemic, most areas were locked down, restricting the outdoor activities for many citizens. Therefore, the demand for hired bikes (sample sizes) decreases for May 2020 and June 2020. However, we observed more extreme values (>= 60K) compared to the previous years. One possible explanation is that due to the social distancing rules, people who have mandatory need of commuting have to choose a safer transportation mean. Therefore, a large number of people are shifting from using public transport like railway to bikes. However, the challenge I want you to work on is to reproduce the following two graphs.
monthly_bike_total <- bike %>%
filter(year >= 2016, year <= 2019) %>%
group_by(month) %>%
summarize(average_bikes = mean(bikes_hired))
monthly_bike_by_year <- bike %>%
filter(year >= 2016, year <= 2021) %>%
group_by(year, month) %>%
summarize(average_bikes = mean(bikes_hired))
monthly_bike_by_year$total_average = monthly_bike_total$average_bikes[match(monthly_bike_by_year$month, monthly_bike_total$month)]
monthly_bike_by_year <- monthly_bike_by_year %>%
mutate(upper_bar = ifelse(average_bikes > total_average, average_bikes - total_average, 0), lower_bar = ifelse(average_bikes < total_average, total_average - average_bikes, 0))
ggplot(monthly_bike_by_year, aes(x = month)) +
geom_ribbon(aes(ymin = total_average, ymax = total_average + upper_bar),
fill = "green", alpha = 0.2, group = 1) +
geom_ribbon(aes(ymin = total_average - lower_bar, ymax = total_average),
fill = "red", alpha = 0.2, group = 1) +
geom_line(aes(y = average_bikes), color = "black",alpha=0.3, group = 1, size = 0.3) +
geom_line(aes(y = total_average), color = "blue", group = 1, size = 0.5) +
facet_wrap(~ year) +
labs(title = "Monthly changes in TfL bike rentals",
subtitle = "Change from monthly average shown in blue and calculated between 2016 and 2019",
y = "Bike rentals",
caption = "Source: TfL, London Data Store") +
scale_x_discrete(guide = guide_axis(n.dodge=1)) +
theme_minimal() +
NULL
The second one looks at percentage changes from the expected level of weekly rentals. The two grey shaded rectangles correspond to Q2 (weeks 14-26) and Q4 (weeks 40-52).
weekly_bike_total <- bike %>%
filter(year >= 2016, year <= 2019) %>%
group_by(week) %>%
summarize(average_bikes = mean(bikes_hired))
weekly_bike_by_year <- bike %>%
filter(year >= 2016, year <= 2021) %>%
group_by(year, week) %>%
summarize(average_bikes = mean(bikes_hired))
weekly_bike_by_year$total_average = weekly_bike_total$average_bikes[match(weekly_bike_by_year$week, weekly_bike_total$week)]
weekly_bike_by_year <- weekly_bike_by_year %>%
mutate(upper_pct = ifelse(average_bikes > total_average, ((average_bikes / total_average) - 1), 0), lower_pct = ifelse(average_bikes < total_average, ((total_average / average_bikes) - 1), 0))
ggplot(weekly_bike_by_year, aes(x = week)) +
geom_ribbon(aes(ymin = 0, ymax = 0 + upper_pct),
fill = "green", alpha = 0.2, group = 1) +
geom_ribbon(aes(ymin = 0 - lower_pct, ymax = 0),
fill = "red", alpha = 0.2, group = 1) +
geom_line(aes(y = upper_pct), color = "black", alpha = 0.3, group = 1, size = 0.1) +
geom_line(aes(y = -lower_pct), color = "black", alpha = 0.3, group = 1, size = 0.1) +
facet_wrap(~ year) +
labs(title = "Weekly changes in TfL bike rentals",
subtitle = "Change from Weekly average shown in blue and calculated between 2016 and 2019",
y = " ",
caption = "Source: TfL, London Data Store") +
geom_rug(sides="b",alpha = 1/2, color=ifelse(weekly_bike_by_year$average_bikes > weekly_bike_by_year$total_average, "green", "red"))+
scale_y_continuous(labels = scales::percent)+
annotate("rect", xmin = 14, xmax = 26, ymin = -1, ymax = 1, fill = "grey", alpha=0.3) +
annotate("rect", xmin = 40, xmax = 52,ymin = -1, ymax = 1, fill = "grey",alpha=0.3) +
theme_minimal() +
scale_x_continuous(breaks=c(13,26,39,53))
NULL## NULL
For both of these graphs, you have to calculate the expected number of rentals per week or month between 2016-2019 and then, see how each week/month of 2020-2021 compares to the expected rentals. Think of the calculation excess_rentals = actual_rentals - expected_rentals.
Should you use the mean or the median to calculate your expected rentals? Why? We should use mean to calculate the expected rentals. Rentals are highly sensitive to seasonality; therefore, we would derive many outliers for excess rentals if we use median as the expected rentals. On the other hand, if we use mean to calculate the expected rentals, the difference can be diluted, which would make the graph more visible. In creating your plots, you may find these links useful:
5 Challenge 2: How has the CPI and its components changed over the last few years?
Remember how we used the tidyqant package to download CPI data. In this exercise, I would like you to do the following:
- You can find CPI components at FRED. You should adapt the code from German polls to scrape the FRED website and pull all of the CPI components into a vector. FIY, the list of components is the second table in that webpage.
- Once you have a vector of components, you can then pass it to
tidyquant::tq_get(get = "economic.data", from = "2000-01-01")to get all data since January 1, 2000 - Since the data you download is an index with various starting dates, you need to calculate the yearly, or 12-month change. To do this you need to use the
lagfunction, and specifically,year_change = value/lag(value, 12) - 1; this means you are comparing the current month’s value with that 12 months ago lag(value, 12). - I want you to order components so the higher the yearly change, the earlier does that component appear.
- You should also make sure that the All Items CPI (CPIAUCSL) appears first.
- Add a
geom_smooth()for each component to get a sense of the overall trend. 1 You may want to colour the points according to whether yearly change was positive or negative.
Having done this, you should get this graph.
CPI_components <- read.csv(file = '/Users/sanskritibajaj/Desktop/cpi_data.csv') #Load the dataset
CPI_components_table <- CPI_components %>% #change the format of date
mutate(date = lubridate::ymd(date))
CPI_components_table <- subset(CPI_components_table, date > "1999-12-31") #filter dates CPI_components_table <- CPI_components_table %>% mutate(year_change = value/lag(value,12) -1) #calculating year on year change
CPI_components_table<- CPI_components_table %>%
arrange(year_change)
CPI_components_table$Category = str_sub(CPI_components_table$title,47,-22) #extracting category namep1 <- ggplot(CPI_components_table, aes(x=date, y = year_change))+ #plotting the yearly change in CPI for each category
scale_y_continuous(labels=scales::percent) +
facet_wrap(~Category)+
geom_point(data = CPI_components_table, aes(x=date, y = year_change,color = year_change<0),show.legend = FALSE)+
geom_smooth() +
theme_bw() +
labs (
title = "Yearly change of US CPI(All Items) and its components", x="Year", y="YoY % Change")
p2 <- p1 + theme(axis.text.x = element_text(angle = 90, hjust = 1))
p2
This graphs is fine, but perhaps has too many sub-categories. You can find the relative importance of components in the Consumer Price Indexes: U.S. city average, December 2020 here. Can you choose a smaller subset of the components you have and only list the major categories (Housing, Transportation, Food and beverages, Medical care, Education and communication, Recreation, and Apparel), sorted according to their relative importance?
Category_Labels = c("Housing","Transportation","Food and Beverages","Apparel","Medical Care", "Education and Communication","Recreation") #Listing the specific categories for which data is required
CPI_components_subset_table <- subset(CPI_components_table, Category == Category_Labels) #filtering data for specific categories
CPI_components_subset_table_sorted <- CPI_components_subset_table %>% #arranging the data as per categories and in dates
arrange(Category, date)
CategoryOrder = c("Food and Beverages", "Apparel","Medical Care","Housing","Education and Communication","Transportation","Recreation") #arranging the data as per the requirement of each category
CPI_components_sorted_ordered <- CPI_components_subset_table_sorted[ order(match(CPI_components_subset_table_sorted$Category, CategoryOrder)), ]
CPI_components_sorted_ordered## component date value
## 83 CPIFABSL 2000-04-01 167
## 84 CPIFABSL 2001-07-01 174
## 85 CPIFABSL 2001-11-01 175
## 86 CPIFABSL 2002-01-01 176
## 87 CPIFABSL 2002-10-01 177
## 88 CPIFABSL 2003-05-01 179
## 89 CPIFABSL 2003-07-01 180
## 90 CPIFABSL 2003-12-01 184
## 91 CPIFABSL 2004-05-01 186
## 92 CPIFABSL 2004-10-01 188
## 93 CPIFABSL 2005-05-01 191
## 94 CPIFABSL 2005-10-01 192
## 95 CPIFABSL 2007-01-01 199
## 96 CPIFABSL 2008-03-01 210
## 97 CPIFABSL 2009-02-01 219
## 98 CPIFABSL 2010-05-01 220
## 99 CPIFABSL 2012-01-01 232
## 100 CPIFABSL 2012-03-01 233
## 101 CPIFABSL 2013-03-01 236
## 102 CPIFABSL 2013-07-01 237
## 103 CPIFABSL 2014-08-01 244
## 104 CPIFABSL 2015-02-01 246
## 105 CPIFABSL 2015-08-01 247
## 106 CPIFABSL 2015-10-01 248
## 107 CPIFABSL 2016-05-01 248
## 108 CPIFABSL 2016-09-01 248
## 109 CPIFABSL 2017-07-01 250
## 110 CPIFABSL 2018-02-01 252
## 111 CPIFABSL 2018-05-01 253
## 112 CPIFABSL 2018-08-01 254
## 113 CPIFABSL 2018-11-01 255
## 114 CPIFABSL 2018-12-01 256
## 115 CPIFABSL 2020-07-01 268
## 116 CPIFABSL 2021-02-01 271
## 1 CPIAPPSL 2000-05-01 130
## 2 CPIAPPSL 2001-07-01 127
## 3 CPIAPPSL 2001-12-01 125
## 4 CPIAPPSL 2002-12-01 123
## 5 CPIAPPSL 2003-03-01 121
## 6 CPIAPPSL 2003-09-01 121
## 7 CPIAPPSL 2004-12-01 120
## 8 CPIAPPSL 2005-02-01 120
## 9 CPIAPPSL 2005-04-01 120
## 10 CPIAPPSL 2006-05-01 120
## 11 CPIAPPSL 2007-11-01 119
## 12 CPIAPPSL 2009-01-01 118
## 13 CPIAPPSL 2010-03-01 120
## 14 CPIAPPSL 2010-08-01 119
## 15 CPIAPPSL 2011-06-01 122
## 16 CPIAPPSL 2012-10-01 127
## 17 CPIAPPSL 2013-07-01 128
## 18 CPIAPPSL 2013-08-01 127
## 19 CPIAPPSL 2013-11-01 127
## 20 CPIAPPSL 2014-02-01 127
## 21 CPIAPPSL 2014-11-01 127
## 22 CPIAPPSL 2015-02-01 126
## 23 CPIAPPSL 2015-06-01 126
## 24 CPIAPPSL 2015-10-01 125
## 25 CPIAPPSL 2015-11-01 126
## 26 CPIAPPSL 2016-02-01 126
## 27 CPIAPPSL 2016-10-01 127
## 28 CPIAPPSL 2016-11-01 127
## 29 CPIAPPSL 2017-02-01 126
## 30 CPIAPPSL 2017-03-01 127
## 31 CPIAPPSL 2017-08-01 125
## 32 CPIAPPSL 2017-09-01 126
## 33 CPIAPPSL 2019-10-01 122
## 34 CPIAPPSL 2020-01-01 123
## 35 CPIAPPSL 2020-08-01 117
## 36 CPIAPPSL 2020-12-01 118
## 37 CPIAPPSL 2021-02-01 119
## 38 CPIAPPSL 2021-06-01 122
## 39 CPIAPPSL 2021-08-01 122
## 165 CPIMEDSL 2000-07-01 261
## 166 CPIMEDSL 2001-04-01 270
## 167 CPIMEDSL 2002-06-01 285
## 168 CPIMEDSL 2002-08-01 287
## 169 CPIMEDSL 2003-08-01 298
## 170 CPIMEDSL 2004-05-01 309
## 171 CPIMEDSL 2004-08-01 312
## 172 CPIMEDSL 2005-01-01 317
## 173 CPIMEDSL 2005-04-01 321
## 174 CPIMEDSL 2005-07-01 324
## 175 CPIMEDSL 2007-06-01 350
## 176 CPIMEDSL 2008-01-01 360
## 177 CPIMEDSL 2008-02-01 361
## 178 CPIMEDSL 2008-06-01 364
## 179 CPIMEDSL 2008-07-01 364
## 180 CPIMEDSL 2010-04-01 387
## 181 CPIMEDSL 2011-05-01 399
## 182 CPIMEDSL 2011-06-01 399
## 183 CPIMEDSL 2011-07-01 401
## 184 CPIMEDSL 2013-02-01 422
## 185 CPIMEDSL 2013-08-01 427
## 186 CPIMEDSL 2013-10-01 429
## 187 CPIMEDSL 2014-01-01 430
## 188 CPIMEDSL 2014-06-01 435
## 189 CPIMEDSL 2014-07-01 436
## 190 CPIMEDSL 2014-08-01 436
## 191 CPIMEDSL 2016-03-01 458
## 192 CPIMEDSL 2017-05-01 473
## 193 CPIMEDSL 2017-11-01 478
## 194 CPIMEDSL 2018-03-01 483
## 195 CPIMEDSL 2018-06-01 486
## 196 CPIMEDSL 2018-09-01 485
## 197 CPIMEDSL 2019-11-01 508
## 198 CPIMEDSL 2020-08-01 524
## 199 CPIMEDSL 2020-09-01 523
## 117 CPIHOSSL 2000-03-01 168
## 118 CPIHOSSL 2000-07-01 170
## 119 CPIHOSSL 2001-08-01 177
## 120 CPIHOSSL 2001-09-01 177
## 121 CPIHOSSL 2001-12-01 178
## 122 CPIHOSSL 2002-04-01 180
## 123 CPIHOSSL 2002-08-01 181
## 124 CPIHOSSL 2002-11-01 182
## 125 CPIHOSSL 2003-09-01 186
## 126 CPIHOSSL 2003-11-01 186
## 127 CPIHOSSL 2004-01-01 187
## 128 CPIHOSSL 2004-05-01 189
## 129 CPIHOSSL 2005-03-01 194
## 130 CPIHOSSL 2005-05-01 195
## 131 CPIHOSSL 2005-10-01 199
## 132 CPIHOSSL 2005-12-01 200
## 133 CPIHOSSL 2006-09-01 205
## 134 CPIHOSSL 2006-12-01 206
## 135 CPIHOSSL 2007-01-01 207
## 136 CPIHOSSL 2007-03-01 208
## 137 CPIHOSSL 2007-07-01 210
## 138 CPIHOSSL 2007-08-01 210
## 139 CPIHOSSL 2009-02-01 218
## 140 CPIHOSSL 2009-06-01 217
## 141 CPIHOSSL 2009-10-01 217
## 142 CPIHOSSL 2010-03-01 216
## 143 CPIHOSSL 2010-12-01 217
## 144 CPIHOSSL 2011-04-01 218
## 145 CPIHOSSL 2012-01-01 221
## 146 CPIHOSSL 2012-09-01 223
## 147 CPIHOSSL 2012-11-01 225
## 148 CPIHOSSL 2013-10-01 229
## 149 CPIHOSSL 2013-11-01 229
## 150 CPIHOSSL 2013-12-01 230
## 151 CPIHOSSL 2014-02-01 231
## 152 CPIHOSSL 2014-04-01 232
## 153 CPIHOSSL 2015-02-01 236
## 154 CPIHOSSL 2015-03-01 237
## 155 CPIHOSSL 2017-01-01 248
## 156 CPIHOSSL 2017-02-01 249
## 157 CPIHOSSL 2017-05-01 250
## 158 CPIHOSSL 2018-10-01 260
## 159 CPIHOSSL 2019-06-01 266
## 160 CPIHOSSL 2020-06-01 271
## 161 CPIHOSSL 2020-10-01 273
## 162 CPIHOSSL 2020-12-01 274
## 163 CPIHOSSL 2021-01-01 274
## 164 CPIHOSSL 2021-07-01 281
## 40 CPIEDUSL 2000-02-01 102
## 41 CPIEDUSL 2000-05-01 102
## 42 CPIEDUSL 2002-08-01 109
## 43 CPIEDUSL 2002-12-01 109
## 44 CPIEDUSL 2003-11-01 110
## 45 CPIEDUSL 2004-01-01 111
## 46 CPIEDUSL 2004-03-01 111
## 47 CPIEDUSL 2004-05-01 111
## 48 CPIEDUSL 2004-07-01 112
## 49 CPIEDUSL 2005-06-01 114
## 50 CPIEDUSL 2005-12-01 115
## 51 CPIEDUSL 2008-05-01 123
## 52 CPIEDUSL 2008-10-01 125
## 53 CPIEDUSL 2008-11-01 125
## 54 CPIEDUSL 2008-12-01 126
## 55 CPIEDUSL 2009-09-01 128
## 56 CPIEDUSL 2010-02-01 129
## 57 CPIEDUSL 2010-05-01 130
## 58 CPIEDUSL 2010-10-01 130
## 59 CPIEDUSL 2010-12-01 130
## 60 CPIEDUSL 2011-05-01 131
## 61 CPIEDUSL 2011-07-01 131
## 62 CPIEDUSL 2011-08-01 132
## 63 CPIEDUSL 2012-08-01 134
## 64 CPIEDUSL 2012-09-01 134
## 65 CPIEDUSL 2013-10-01 136
## 66 CPIEDUSL 2014-06-01 138
## 67 CPIEDUSL 2014-08-01 138
## 68 CPIEDUSL 2014-12-01 137
## 69 CPIEDUSL 2016-05-01 139
## 70 CPIEDUSL 2016-07-01 139
## 71 CPIEDUSL 2016-08-01 139
## 72 CPIEDUSL 2016-10-01 139
## 73 CPIEDUSL 2016-11-01 139
## 74 CPIEDUSL 2018-03-01 136
## 75 CPIEDUSL 2018-05-01 137
## 76 CPIEDUSL 2019-03-01 137
## 77 CPIEDUSL 2019-10-01 138
## 78 CPIEDUSL 2019-12-01 139
## 79 CPIEDUSL 2020-04-01 140
## 80 CPIEDUSL 2021-03-01 141
## 81 CPIEDUSL 2021-07-01 143
## 82 CPIEDUSL 2021-08-01 143
## 242 CPITRNSL 2000-10-01 155
## 243 CPITRNSL 2000-12-01 155
## 244 CPITRNSL 2002-01-01 149
## 245 CPITRNSL 2002-07-01 153
## 246 CPITRNSL 2003-12-01 157
## 247 CPITRNSL 2005-03-01 169
## 248 CPITRNSL 2005-04-01 170
## 249 CPITRNSL 2006-02-01 178
## 250 CPITRNSL 2006-03-01 178
## 251 CPITRNSL 2008-01-01 194
## 252 CPITRNSL 2008-02-01 195
## 253 CPITRNSL 2008-10-01 196
## 254 CPITRNSL 2008-11-01 176
## 255 CPITRNSL 2009-04-01 170
## 256 CPITRNSL 2010-03-01 191
## 257 CPITRNSL 2010-08-01 192
## 258 CPITRNSL 2010-10-01 197
## 259 CPITRNSL 2011-01-01 204
## 260 CPITRNSL 2012-03-01 218
## 261 CPITRNSL 2012-09-01 222
## 262 CPITRNSL 2013-03-01 219
## 263 CPITRNSL 2014-05-01 218
## 264 CPITRNSL 2015-06-01 204
## 265 CPITRNSL 2016-01-01 193
## 266 CPITRNSL 2016-02-01 189
## 267 CPITRNSL 2016-08-01 194
## 268 CPITRNSL 2019-02-01 206
## 269 CPITRNSL 2019-06-01 210
## 270 CPITRNSL 2019-09-01 209
## 271 CPITRNSL 2020-02-01 209
## 272 CPITRNSL 2020-07-01 198
## 273 CPITRNSL 2020-11-01 205
## 274 CPITRNSL 2021-02-01 211
## 200 CPIRECSL 2000-12-01 104
## 201 CPIRECSL 2001-04-01 105
## 202 CPIRECSL 2001-06-01 105
## 203 CPIRECSL 2002-02-01 106
## 204 CPIRECSL 2002-04-01 106
## 205 CPIRECSL 2002-07-01 106
## 206 CPIRECSL 2002-09-01 106
## 207 CPIRECSL 2002-10-01 106
## 208 CPIRECSL 2003-01-01 107
## 209 CPIRECSL 2003-12-01 108
## 210 CPIRECSL 2004-10-01 109
## 211 CPIRECSL 2005-01-01 109
## 212 CPIRECSL 2005-09-01 110
## 213 CPIRECSL 2006-08-01 111
## 214 CPIRECSL 2006-12-01 111
## 215 CPIRECSL 2008-03-01 113
## 216 CPIRECSL 2008-07-01 113
## 217 CPIRECSL 2008-11-01 114
## 218 CPIRECSL 2008-12-01 114
## 219 CPIRECSL 2009-01-01 114
## 220 CPIRECSL 2009-05-01 114
## 221 CPIRECSL 2009-12-01 114
## 222 CPIRECSL 2010-06-01 114
## 223 CPIRECSL 2011-05-01 113
## 224 CPIRECSL 2011-09-01 113
## 225 CPIRECSL 2011-12-01 114
## 226 CPIRECSL 2012-10-01 115
## 227 CPIRECSL 2012-12-01 115
## 228 CPIRECSL 2014-11-01 115
## 229 CPIRECSL 2015-02-01 115
## 230 CPIRECSL 2015-09-01 116
## 231 CPIRECSL 2015-12-01 116
## 232 CPIRECSL 2016-05-01 117
## 233 CPIRECSL 2016-10-01 117
## 234 CPIRECSL 2017-01-01 117
## 235 CPIRECSL 2017-03-01 118
## 236 CPIRECSL 2017-09-01 119
## 237 CPIRECSL 2018-04-01 119
## 238 CPIRECSL 2018-05-01 119
## 239 CPIRECSL 2020-02-01 122
## 240 CPIRECSL 2020-04-01 122
## 241 CPIRECSL 2021-05-01 125
## title
## 83 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 84 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 85 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 86 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 87 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 88 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 89 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 90 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 91 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 92 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 93 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 94 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 95 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 96 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 97 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 98 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 99 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 100 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 101 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 102 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 103 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 104 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 105 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 106 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 107 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 108 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 109 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 110 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 111 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 112 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 113 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 114 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 115 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 116 Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
## 1 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 2 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 3 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 4 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 5 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 6 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 7 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 8 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 9 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 10 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 11 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 12 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 13 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 14 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 15 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 16 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 17 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 18 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 19 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 20 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 21 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 22 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 23 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 24 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 25 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 26 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 27 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 28 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 29 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 30 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 31 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 32 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 33 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 34 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 35 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 36 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 37 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 38 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 39 Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
## 165 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 166 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 167 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 168 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 169 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 170 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 171 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 172 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 173 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 174 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 175 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 176 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 177 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 178 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 179 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 180 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 181 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 182 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 183 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 184 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 185 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 186 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 187 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 188 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 189 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 190 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 191 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 192 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 193 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 194 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 195 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 196 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 197 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 198 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 199 Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
## 117 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 118 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 119 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 120 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 121 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 122 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 123 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 124 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 125 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 126 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 127 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 128 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 129 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 130 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 131 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 132 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 133 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 134 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 135 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 136 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 137 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 138 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 139 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 140 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 141 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 142 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 143 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 144 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 145 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 146 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 147 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 148 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 149 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 150 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 151 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 152 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 153 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 154 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 155 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 156 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 157 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 158 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 159 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 160 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 161 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 162 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 163 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 164 Consumer Price Index for All Urban Consumers: Housing in U.S. City Average
## 40 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 41 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 42 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 43 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 44 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 45 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 46 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 47 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 48 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 49 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 50 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 51 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 52 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 53 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 54 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 55 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 56 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 57 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 58 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 59 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 60 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 61 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 62 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 63 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 64 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 65 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 66 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 67 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 68 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 69 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 70 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 71 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 72 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 73 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 74 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 75 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 76 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 77 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 78 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 79 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 80 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 81 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 82 Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
## 242 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 243 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 244 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 245 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 246 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 247 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 248 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 249 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 250 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 251 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 252 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 253 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 254 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 255 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 256 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 257 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 258 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 259 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 260 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 261 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 262 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 263 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 264 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 265 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 266 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 267 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 268 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 269 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 270 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 271 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 272 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 273 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 274 Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average
## 200 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 201 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 202 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 203 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 204 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 205 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 206 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 207 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 208 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 209 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 210 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 211 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 212 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 213 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 214 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 215 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 216 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 217 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 218 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 219 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 220 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 221 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 222 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 223 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 224 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 225 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 226 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 227 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 228 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 229 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 230 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 231 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 232 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 233 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 234 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 235 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 236 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 237 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 238 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 239 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 240 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## 241 Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
## vintage_date units freq seas_adj updated year_change
## 83 Current Index 1982-1984=100 M SA 14-09-21 -0.275127
## 84 Current Index 1982-1984=100 M SA 14-09-21 0.031398
## 85 Current Index 1982-1984=100 M SA 14-09-21 0.033589
## 86 Current Index 1982-1984=100 M SA 14-09-21 0.028672
## 87 Current Index 1982-1984=100 M SA 14-09-21 0.009698
## 88 Current Index 1982-1984=100 M SA 14-09-21 0.017007
## 89 Current Index 1982-1984=100 M SA 14-09-21 0.020939
## 90 Current Index 1982-1984=100 M SA 14-09-21 0.035453
## 91 Current Index 1982-1984=100 M SA 14-09-21 0.039576
## 92 Current Index 1982-1984=100 M SA 14-09-21 0.033480
## 93 Current Index 1982-1984=100 M SA 14-09-21 0.024129
## 94 Current Index 1982-1984=100 M SA 14-09-21 0.021774
## 95 Current Index 1982-1984=100 M SA 14-09-21 0.024281
## 96 Current Index 1982-1984=100 M SA 14-09-21 0.043524
## 97 Current Index 1982-1984=100 M SA 14-09-21 0.047207
## 98 Current Index 1982-1984=100 M SA 14-09-21 0.007328
## 99 Current Index 1982-1984=100 M SA 14-09-21 0.042185
## 100 Current Index 1982-1984=100 M SA 14-09-21 0.031797
## 101 Current Index 1982-1984=100 M SA 14-09-21 0.015604
## 102 Current Index 1982-1984=100 M SA 14-09-21 0.014464
## 103 Current Index 1982-1984=100 M SA 14-09-21 0.025686
## 104 Current Index 1982-1984=100 M SA 14-09-21 0.028460
## 105 Current Index 1982-1984=100 M SA 14-09-21 0.015564
## 106 Current Index 1982-1984=100 M SA 14-09-21 0.015493
## 107 Current Index 1982-1984=100 M SA 14-09-21 0.006869
## 108 Current Index 1982-1984=100 M SA 14-09-21 -0.001839
## 109 Current Index 1982-1984=100 M SA 14-09-21 0.010614
## 110 Current Index 1982-1984=100 M SA 14-09-21 0.014160
## 111 Current Index 1982-1984=100 M SA 14-09-21 0.012095
## 112 Current Index 1982-1984=100 M SA 14-09-21 0.014246
## 113 Current Index 1982-1984=100 M SA 14-09-21 0.014503
## 114 Current Index 1982-1984=100 M SA 14-09-21 0.016105
## 115 Current Index 1982-1984=100 M SA 14-09-21 0.038950
## 116 Current Index 1982-1984=100 M SA 14-09-21 0.035163
## 1 Current Index 1982-1984=100 M SA 14-09-21 -0.505398
## 2 Current Index 1982-1984=100 M SA 14-09-21 -0.012413
## 3 Current Index 1982-1984=100 M SA 14-09-21 -0.032533
## 4 Current Index 1982-1984=100 M SA 14-09-21 -0.017614
## 5 Current Index 1982-1984=100 M SA 14-09-21 -0.036712
## 6 Current Index 1982-1984=100 M SA 14-09-21 -0.020259
## 7 Current Index 1982-1984=100 M SA 14-09-21 -0.003328
## 8 Current Index 1982-1984=100 M SA 14-09-21 -0.000833
## 9 Current Index 1982-1984=100 M SA 14-09-21 -0.005804
## 10 Current Index 1982-1984=100 M SA 14-09-21 0.000000
## 11 Current Index 1982-1984=100 M SA 14-09-21 -0.005553
## 12 Current Index 1982-1984=100 M SA 14-09-21 -0.012316
## 13 Current Index 1982-1984=100 M SA 14-09-21 -0.001188
## 14 Current Index 1982-1984=100 M SA 14-09-21 -0.010612
## 15 Current Index 1982-1984=100 M SA 14-09-21 0.017446
## 16 Current Index 1982-1984=100 M SA 14-09-21 0.028406
## 17 Current Index 1982-1984=100 M SA 14-09-21 0.013578
## 18 Current Index 1982-1984=100 M SA 14-09-21 0.016588
## 19 Current Index 1982-1984=100 M SA 14-09-21 0.001116
## 20 Current Index 1982-1984=100 M SA 14-09-21 -0.006673
## 21 Current Index 1982-1984=100 M SA 14-09-21 -0.000620
## 22 Current Index 1982-1984=100 M SA 14-09-21 -0.010363
## 23 Current Index 1982-1984=100 M SA 14-09-21 -0.019315
## 24 Current Index 1982-1984=100 M SA 14-09-21 -0.019083
## 25 Current Index 1982-1984=100 M SA 14-09-21 -0.012023
## 26 Current Index 1982-1984=100 M SA 14-09-21 -0.000492
## 27 Current Index 1982-1984=100 M SA 14-09-21 0.009559
## 28 Current Index 1982-1984=100 M SA 14-09-21 0.007007
## 29 Current Index 1982-1984=100 M SA 14-09-21 0.000953
## 30 Current Index 1982-1984=100 M SA 14-09-21 0.006063
## 31 Current Index 1982-1984=100 M SA 14-09-21 -0.006413
## 32 Current Index 1982-1984=100 M SA 14-09-21 -0.002462
## 33 Current Index 1982-1984=100 M SA 14-09-21 -0.022319
## 34 Current Index 1982-1984=100 M SA 14-09-21 -0.014131
## 35 Current Index 1982-1984=100 M SA 14-09-21 -0.059704
## 36 Current Index 1982-1984=100 M SA 14-09-21 -0.040986
## 37 Current Index 1982-1984=100 M SA 14-09-21 -0.035683
## 38 Current Index 1982-1984=100 M SA 14-09-21 0.048622
## 39 Current Index 1982-1984=100 M SA 14-09-21 0.041963
## 165 Current Index 1982-1984=100 M SA 14-09-21 0.846746
## 166 Current Index 1982-1984=100 M SA 14-09-21 0.046440
## 167 Current Index 1982-1984=100 M SA 14-09-21 0.044787
## 168 Current Index 1982-1984=100 M SA 14-09-21 0.047029
## 169 Current Index 1982-1984=100 M SA 14-09-21 0.038649
## 170 Current Index 1982-1984=100 M SA 14-09-21 0.045393
## 171 Current Index 1982-1984=100 M SA 14-09-21 0.045256
## 172 Current Index 1982-1984=100 M SA 14-09-21 0.043120
## 173 Current Index 1982-1984=100 M SA 14-09-21 0.041910
## 174 Current Index 1982-1984=100 M SA 14-09-21 0.042793
## 175 Current Index 1982-1984=100 M SA 14-09-21 0.040217
## 176 Current Index 1982-1984=100 M SA 14-09-21 0.049142
## 177 Current Index 1982-1984=100 M SA 14-09-21 0.044592
## 178 Current Index 1982-1984=100 M SA 14-09-21 0.040508
## 179 Current Index 1982-1984=100 M SA 14-09-21 0.035361
## 180 Current Index 1982-1984=100 M SA 14-09-21 0.036218
## 181 Current Index 1982-1984=100 M SA 14-09-21 0.030037
## 182 Current Index 1982-1984=100 M SA 14-09-21 0.028924
## 183 Current Index 1982-1984=100 M SA 14-09-21 0.031990
## 184 Current Index 1982-1984=100 M SA 14-09-21 0.031087
## 185 Current Index 1982-1984=100 M SA 14-09-21 0.023022
## 186 Current Index 1982-1984=100 M SA 14-09-21 0.022707
## 187 Current Index 1982-1984=100 M SA 14-09-21 0.021075
## 188 Current Index 1982-1984=100 M SA 14-09-21 0.025693
## 189 Current Index 1982-1984=100 M SA 14-09-21 0.025543
## 190 Current Index 1982-1984=100 M SA 14-09-21 0.020772
## 191 Current Index 1982-1984=100 M SA 14-09-21 0.033368
## 192 Current Index 1982-1984=100 M SA 14-09-21 0.026939
## 193 Current Index 1982-1984=100 M SA 14-09-21 0.016626
## 194 Current Index 1982-1984=100 M SA 14-09-21 0.019810
## 195 Current Index 1982-1984=100 M SA 14-09-21 0.024638
## 196 Current Index 1982-1984=100 M SA 14-09-21 0.017229
## 197 Current Index 1982-1984=100 M SA 14-09-21 0.042434
## 198 Current Index 1982-1984=100 M SA 14-09-21 0.044675
## 199 Current Index 1982-1984=100 M SA 14-09-21 0.042107
## 117 Current Index 1982-1984=100 M SA 14-09-21 0.313162
## 118 Current Index 1982-1984=100 M SA 14-09-21 0.326888
## 119 Current Index 1982-1984=100 M SA 14-09-21 0.040541
## 120 Current Index 1982-1984=100 M SA 14-09-21 0.034483
## 121 Current Index 1982-1984=100 M SA 14-09-21 0.029514
## 122 Current Index 1982-1984=100 M SA 14-09-21 0.023375
## 123 Current Index 1982-1984=100 M SA 14-09-21 0.020327
## 124 Current Index 1982-1984=100 M SA 14-09-21 0.025352
## 125 Current Index 1982-1984=100 M SA 14-09-21 0.024296
## 126 Current Index 1982-1984=100 M SA 14-09-21 0.021978
## 127 Current Index 1982-1984=100 M SA 14-09-21 0.021870
## 128 Current Index 1982-1984=100 M SA 14-09-21 0.023294
## 129 Current Index 1982-1984=100 M SA 14-09-21 0.033031
## 130 Current Index 1982-1984=100 M SA 14-09-21 0.030175
## 131 Current Index 1982-1984=100 M SA 14-09-21 0.039226
## 132 Current Index 1982-1984=100 M SA 14-09-21 0.039042
## 133 Current Index 1982-1984=100 M SA 14-09-21 0.040142
## 134 Current Index 1982-1984=100 M SA 14-09-21 0.033066
## 135 Current Index 1982-1984=100 M SA 14-09-21 0.031362
## 136 Current Index 1982-1984=100 M SA 14-09-21 0.034389
## 137 Current Index 1982-1984=100 M SA 14-09-21 0.031185
## 138 Current Index 1982-1984=100 M SA 14-09-21 0.028927
## 139 Current Index 1982-1984=100 M SA 14-09-21 0.019618
## 140 Current Index 1982-1984=100 M SA 14-09-21 0.000420
## 141 Current Index 1982-1984=100 M SA 14-09-21 -0.003505
## 142 Current Index 1982-1984=100 M SA 14-09-21 -0.006033
## 143 Current Index 1982-1984=100 M SA 14-09-21 -0.000350
## 144 Current Index 1982-1984=100 M SA 14-09-21 0.009690
## 145 Current Index 1982-1984=100 M SA 14-09-21 0.018589
## 146 Current Index 1982-1984=100 M SA 14-09-21 0.015040
## 147 Current Index 1982-1984=100 M SA 14-09-21 0.017305
## 148 Current Index 1982-1984=100 M SA 14-09-21 0.020697
## 149 Current Index 1982-1984=100 M SA 14-09-21 0.020556
## 150 Current Index 1982-1984=100 M SA 14-09-21 0.021650
## 151 Current Index 1982-1984=100 M SA 14-09-21 0.024399
## 152 Current Index 1982-1984=100 M SA 14-09-21 0.025254
## 153 Current Index 1982-1984=100 M SA 14-09-21 0.022139
## 154 Current Index 1982-1984=100 M SA 14-09-21 0.019313
## 155 Current Index 1982-1984=100 M SA 14-09-21 0.031150
## 156 Current Index 1982-1984=100 M SA 14-09-21 0.031685
## 157 Current Index 1982-1984=100 M SA 14-09-21 0.031071
## 158 Current Index 1982-1984=100 M SA 14-09-21 0.028187
## 159 Current Index 1982-1984=100 M SA 14-09-21 0.030262
## 160 Current Index 1982-1984=100 M SA 14-09-21 0.020485
## 161 Current Index 1982-1984=100 M SA 14-09-21 0.019402
## 162 Current Index 1982-1984=100 M SA 14-09-21 0.020137
## 163 Current Index 1982-1984=100 M SA 14-09-21 0.017897
## 164 Current Index 1982-1984=100 M SA 14-09-21 0.033399
## 40 Current Index Dec 1997=100 M SA 14-09-21 0.044797
## 41 Current Index Dec 1997=100 M SA 14-09-21 0.004211
## 42 Current Index Dec 1997=100 M SA 14-09-21 0.029301
## 43 Current Index Dec 1997=100 M SA 14-09-21 0.021556
## 44 Current Index Dec 1997=100 M SA 14-09-21 0.013787
## 45 Current Index Dec 1997=100 M SA 14-09-21 0.012785
## 46 Current Index Dec 1997=100 M SA 14-09-21 0.015511
## 47 Current Index Dec 1997=100 M SA 14-09-21 0.017383
## 48 Current Index Dec 1997=100 M SA 14-09-21 0.018248
## 49 Current Index Dec 1997=100 M SA 14-09-21 0.017937
## 50 Current Index Dec 1997=100 M SA 14-09-21 0.024043
## 51 Current Index Dec 1997=100 M SA 14-09-21 0.029437
## 52 Current Index Dec 1997=100 M SA 14-09-21 0.034602
## 53 Current Index Dec 1997=100 M SA 14-09-21 0.036569
## 54 Current Index Dec 1997=100 M SA 14-09-21 0.036729
## 55 Current Index Dec 1997=100 M SA 14-09-21 0.028339
## 56 Current Index Dec 1997=100 M SA 14-09-21 0.023030
## 57 Current Index Dec 1997=100 M SA 14-09-21 0.021617
## 58 Current Index Dec 1997=100 M SA 14-09-21 0.015144
## 59 Current Index Dec 1997=100 M SA 14-09-21 0.013475
## 60 Current Index Dec 1997=100 M SA 14-09-21 0.009917
## 61 Current Index Dec 1997=100 M SA 14-09-21 0.009192
## 62 Current Index Dec 1997=100 M SA 14-09-21 0.011198
## 63 Current Index Dec 1997=100 M SA 14-09-21 0.015659
## 64 Current Index Dec 1997=100 M SA 14-09-21 0.016183
## 65 Current Index Dec 1997=100 M SA 14-09-21 0.015627
## 66 Current Index Dec 1997=100 M SA 14-09-21 0.015386
## 67 Current Index Dec 1997=100 M SA 14-09-21 0.015616
## 68 Current Index Dec 1997=100 M SA 14-09-21 0.003800
## 69 Current Index Dec 1997=100 M SA 14-09-21 0.011351
## 70 Current Index Dec 1997=100 M SA 14-09-21 0.008391
## 71 Current Index Dec 1997=100 M SA 14-09-21 0.007545
## 72 Current Index Dec 1997=100 M SA 14-09-21 -0.002131
## 73 Current Index Dec 1997=100 M SA 14-09-21 -0.003031
## 74 Current Index Dec 1997=100 M SA 14-09-21 -0.001408
## 75 Current Index Dec 1997=100 M SA 14-09-21 0.005035
## 76 Current Index Dec 1997=100 M SA 14-09-21 0.007747
## 77 Current Index Dec 1997=100 M SA 14-09-21 0.005637
## 78 Current Index Dec 1997=100 M SA 14-09-21 0.013745
## 79 Current Index Dec 1997=100 M SA 14-09-21 0.015943
## 80 Current Index Dec 1997=100 M SA 14-09-21 0.014991
## 81 Current Index Dec 1997=100 M SA 14-09-21 0.011334
## 82 Current Index Dec 1997=100 M SA 14-09-21 0.011992
## 242 Current Index 1982-1984=100 M SA 14-09-21 -0.335832
## 243 Current Index 1982-1984=100 M SA 14-09-21 -0.337548
## 244 Current Index 1982-1984=100 M SA 14-09-21 -0.039254
## 245 Current Index 1982-1984=100 M SA 14-09-21 -0.004545
## 246 Current Index 1982-1984=100 M SA 14-09-21 0.013522
## 247 Current Index 1982-1984=100 M SA 14-09-21 0.052239
## 248 Current Index 1982-1984=100 M SA 14-09-21 0.065707
## 249 Current Index 1982-1984=100 M SA 14-09-21 0.059976
## 250 Current Index 1982-1984=100 M SA 14-09-21 0.052600
## 251 Current Index 1982-1984=100 M SA 14-09-21 0.096250
## 252 Current Index 1982-1984=100 M SA 14-09-21 0.096817
## 253 Current Index 1982-1984=100 M SA 14-09-21 0.046469
## 254 Current Index 1982-1984=100 M SA 14-09-21 -0.087275
## 255 Current Index 1982-1984=100 M SA 14-09-21 -0.128544
## 256 Current Index 1982-1984=100 M SA 14-09-21 0.129396
## 257 Current Index 1982-1984=100 M SA 14-09-21 0.046579
## 258 Current Index 1982-1984=100 M SA 14-09-21 0.048202
## 259 Current Index 1982-1984=100 M SA 14-09-21 0.057561
## 260 Current Index 1982-1984=100 M SA 14-09-21 0.042301
## 261 Current Index 1982-1984=100 M SA 14-09-21 0.028400
## 262 Current Index 1982-1984=100 M SA 14-09-21 0.002417
## 263 Current Index 1982-1984=100 M SA 14-09-21 0.020074
## 264 Current Index 1982-1984=100 M SA 14-09-21 -0.067655
## 265 Current Index 1982-1984=100 M SA 14-09-21 -0.011311
## 266 Current Index 1982-1984=100 M SA 14-09-21 -0.042129
## 267 Current Index 1982-1984=100 M SA 14-09-21 -0.041479
## 268 Current Index 1982-1984=100 M SA 14-09-21 -0.017059
## 269 Current Index 1982-1984=100 M SA 14-09-21 -0.005385
## 270 Current Index 1982-1984=100 M SA 14-09-21 -0.014105
## 271 Current Index 1982-1984=100 M SA 14-09-21 0.016617
## 272 Current Index 1982-1984=100 M SA 14-09-21 -0.056890
## 273 Current Index 1982-1984=100 M SA 14-09-21 -0.034458
## 274 Current Index 1982-1984=100 M SA 14-09-21 0.006305
## 200 Current Index Dec 1997=100 M SA 14-09-21 -0.802178
## 201 Current Index Dec 1997=100 M SA 14-09-21 0.021442
## 202 Current Index Dec 1997=100 M SA 14-09-21 0.012573
## 203 Current Index Dec 1997=100 M SA 14-09-21 0.016315
## 204 Current Index Dec 1997=100 M SA 14-09-21 0.014313
## 205 Current Index Dec 1997=100 M SA 14-09-21 0.011439
## 206 Current Index Dec 1997=100 M SA 14-09-21 0.009497
## 207 Current Index Dec 1997=100 M SA 14-09-21 0.010436
## 208 Current Index Dec 1997=100 M SA 14-09-21 0.011353
## 209 Current Index Dec 1997=100 M SA 14-09-21 0.011236
## 210 Current Index Dec 1997=100 M SA 14-09-21 0.010214
## 211 Current Index Dec 1997=100 M SA 14-09-21 0.010185
## 212 Current Index Dec 1997=100 M SA 14-09-21 0.009200
## 213 Current Index Dec 1997=100 M SA 14-09-21 0.017383
## 214 Current Index Dec 1997=100 M SA 14-09-21 0.010000
## 215 Current Index Dec 1997=100 M SA 14-09-21 0.013234
## 216 Current Index Dec 1997=100 M SA 14-09-21 0.017209
## 217 Current Index Dec 1997=100 M SA 14-09-21 0.020192
## 218 Current Index Dec 1997=100 M SA 14-09-21 0.017711
## 219 Current Index Dec 1997=100 M SA 14-09-21 0.015697
## 220 Current Index Dec 1997=100 M SA 14-09-21 0.011202
## 221 Current Index Dec 1997=100 M SA 14-09-21 -0.003510
## 222 Current Index Dec 1997=100 M SA 14-09-21 -0.007398
## 223 Current Index Dec 1997=100 M SA 14-09-21 -0.000353
## 224 Current Index Dec 1997=100 M SA 14-09-21 0.003245
## 225 Current Index Dec 1997=100 M SA 14-09-21 0.010397
## 226 Current Index Dec 1997=100 M SA 14-09-21 0.013829
## 227 Current Index Dec 1997=100 M SA 14-09-21 0.009465
## 228 Current Index Dec 1997=100 M SA 14-09-21 -0.002532
## 229 Current Index Dec 1997=100 M SA 14-09-21 -0.000606
## 230 Current Index Dec 1997=100 M SA 14-09-21 0.006498
## 231 Current Index Dec 1997=100 M SA 14-09-21 0.006385
## 232 Current Index Dec 1997=100 M SA 14-09-21 0.011593
## 233 Current Index Dec 1997=100 M SA 14-09-21 0.004519
## 234 Current Index Dec 1997=100 M SA 14-09-21 0.010737
## 235 Current Index Dec 1997=100 M SA 14-09-21 0.012720
## 236 Current Index Dec 1997=100 M SA 14-09-21 0.016158
## 237 Current Index Dec 1997=100 M SA 14-09-21 0.003396
## 238 Current Index Dec 1997=100 M SA 14-09-21 0.002922
## 239 Current Index Dec 1997=100 M SA 14-09-21 0.014851
## 240 Current Index Dec 1997=100 M SA 14-09-21 0.009637
## 241 Current Index Dec 1997=100 M SA 14-09-21 0.016195
## Category
## 83 Food and Beverages
## 84 Food and Beverages
## 85 Food and Beverages
## 86 Food and Beverages
## 87 Food and Beverages
## 88 Food and Beverages
## 89 Food and Beverages
## 90 Food and Beverages
## 91 Food and Beverages
## 92 Food and Beverages
## 93 Food and Beverages
## 94 Food and Beverages
## 95 Food and Beverages
## 96 Food and Beverages
## 97 Food and Beverages
## 98 Food and Beverages
## 99 Food and Beverages
## 100 Food and Beverages
## 101 Food and Beverages
## 102 Food and Beverages
## 103 Food and Beverages
## 104 Food and Beverages
## 105 Food and Beverages
## 106 Food and Beverages
## 107 Food and Beverages
## 108 Food and Beverages
## 109 Food and Beverages
## 110 Food and Beverages
## 111 Food and Beverages
## 112 Food and Beverages
## 113 Food and Beverages
## 114 Food and Beverages
## 115 Food and Beverages
## 116 Food and Beverages
## 1 Apparel
## 2 Apparel
## 3 Apparel
## 4 Apparel
## 5 Apparel
## 6 Apparel
## 7 Apparel
## 8 Apparel
## 9 Apparel
## 10 Apparel
## 11 Apparel
## 12 Apparel
## 13 Apparel
## 14 Apparel
## 15 Apparel
## 16 Apparel
## 17 Apparel
## 18 Apparel
## 19 Apparel
## 20 Apparel
## 21 Apparel
## 22 Apparel
## 23 Apparel
## 24 Apparel
## 25 Apparel
## 26 Apparel
## 27 Apparel
## 28 Apparel
## 29 Apparel
## 30 Apparel
## 31 Apparel
## 32 Apparel
## 33 Apparel
## 34 Apparel
## 35 Apparel
## 36 Apparel
## 37 Apparel
## 38 Apparel
## 39 Apparel
## 165 Medical Care
## 166 Medical Care
## 167 Medical Care
## 168 Medical Care
## 169 Medical Care
## 170 Medical Care
## 171 Medical Care
## 172 Medical Care
## 173 Medical Care
## 174 Medical Care
## 175 Medical Care
## 176 Medical Care
## 177 Medical Care
## 178 Medical Care
## 179 Medical Care
## 180 Medical Care
## 181 Medical Care
## 182 Medical Care
## 183 Medical Care
## 184 Medical Care
## 185 Medical Care
## 186 Medical Care
## 187 Medical Care
## 188 Medical Care
## 189 Medical Care
## 190 Medical Care
## 191 Medical Care
## 192 Medical Care
## 193 Medical Care
## 194 Medical Care
## 195 Medical Care
## 196 Medical Care
## 197 Medical Care
## 198 Medical Care
## 199 Medical Care
## 117 Housing
## 118 Housing
## 119 Housing
## 120 Housing
## 121 Housing
## 122 Housing
## 123 Housing
## 124 Housing
## 125 Housing
## 126 Housing
## 127 Housing
## 128 Housing
## 129 Housing
## 130 Housing
## 131 Housing
## 132 Housing
## 133 Housing
## 134 Housing
## 135 Housing
## 136 Housing
## 137 Housing
## 138 Housing
## 139 Housing
## 140 Housing
## 141 Housing
## 142 Housing
## 143 Housing
## 144 Housing
## 145 Housing
## 146 Housing
## 147 Housing
## 148 Housing
## 149 Housing
## 150 Housing
## 151 Housing
## 152 Housing
## 153 Housing
## 154 Housing
## 155 Housing
## 156 Housing
## 157 Housing
## 158 Housing
## 159 Housing
## 160 Housing
## 161 Housing
## 162 Housing
## 163 Housing
## 164 Housing
## 40 Education and Communication
## 41 Education and Communication
## 42 Education and Communication
## 43 Education and Communication
## 44 Education and Communication
## 45 Education and Communication
## 46 Education and Communication
## 47 Education and Communication
## 48 Education and Communication
## 49 Education and Communication
## 50 Education and Communication
## 51 Education and Communication
## 52 Education and Communication
## 53 Education and Communication
## 54 Education and Communication
## 55 Education and Communication
## 56 Education and Communication
## 57 Education and Communication
## 58 Education and Communication
## 59 Education and Communication
## 60 Education and Communication
## 61 Education and Communication
## 62 Education and Communication
## 63 Education and Communication
## 64 Education and Communication
## 65 Education and Communication
## 66 Education and Communication
## 67 Education and Communication
## 68 Education and Communication
## 69 Education and Communication
## 70 Education and Communication
## 71 Education and Communication
## 72 Education and Communication
## 73 Education and Communication
## 74 Education and Communication
## 75 Education and Communication
## 76 Education and Communication
## 77 Education and Communication
## 78 Education and Communication
## 79 Education and Communication
## 80 Education and Communication
## 81 Education and Communication
## 82 Education and Communication
## 242 Transportation
## 243 Transportation
## 244 Transportation
## 245 Transportation
## 246 Transportation
## 247 Transportation
## 248 Transportation
## 249 Transportation
## 250 Transportation
## 251 Transportation
## 252 Transportation
## 253 Transportation
## 254 Transportation
## 255 Transportation
## 256 Transportation
## 257 Transportation
## 258 Transportation
## 259 Transportation
## 260 Transportation
## 261 Transportation
## 262 Transportation
## 263 Transportation
## 264 Transportation
## 265 Transportation
## 266 Transportation
## 267 Transportation
## 268 Transportation
## 269 Transportation
## 270 Transportation
## 271 Transportation
## 272 Transportation
## 273 Transportation
## 274 Transportation
## 200 Recreation
## 201 Recreation
## 202 Recreation
## 203 Recreation
## 204 Recreation
## 205 Recreation
## 206 Recreation
## 207 Recreation
## 208 Recreation
## 209 Recreation
## 210 Recreation
## 211 Recreation
## 212 Recreation
## 213 Recreation
## 214 Recreation
## 215 Recreation
## 216 Recreation
## 217 Recreation
## 218 Recreation
## 219 Recreation
## 220 Recreation
## 221 Recreation
## 222 Recreation
## 223 Recreation
## 224 Recreation
## 225 Recreation
## 226 Recreation
## 227 Recreation
## 228 Recreation
## 229 Recreation
## 230 Recreation
## 231 Recreation
## 232 Recreation
## 233 Recreation
## 234 Recreation
## 235 Recreation
## 236 Recreation
## 237 Recreation
## 238 Recreation
## 239 Recreation
## 240 Recreation
## 241 Recreation6 Deliverables
As usual, there is a lot of explanatory text, comments, etc. You do not need these, so delete them and produce a stand-alone document that you could share with someone. Knit the edited and completed R Markdown file as an HTML document (use the “Knit” button at the top of the script editor window) and upload it to Canvas.
7 Details
- Who did you collaborate with: Philippine Bonnaud, Ieuan Edmonds, Maoze Li, Yuzhou Li, Francesco Sartori and Sanskriti Bajaj
- Approximately how much time did you spend on this problem set: 5 hours
- What, if anything, gave you the most trouble: challenge 2 csv
Please seek out help when you need it, and remember the 15-minute rule. You know enough R (and have enough examples of code from class and your readings) to be able to do this. If you get stuck, ask for help from others, post a question on Slack– and remember that I am here to help too!
As a true test to yourself, do you understand the code you submitted and are you able to explain it to someone else?
8 Rubric
Check minus (1/5): Displays minimal effort. Doesn’t complete all components. Code is poorly written and not documented. Uses the same type of plot for each graph, or doesn’t use plots appropriate for the variables being analyzed.
Check (3/5): Solid effort. Hits all the elements. No clear mistakes. Easy to follow (both the code and the output).
Check plus (5/5): Finished all components of the assignment correctly and addressed both challenges. Code is well-documented (both self-documented and with additional comments as necessary). Used tidyverse, instead of base R. Graphs and tables are properly labelled. Analysis is clear and easy to follow, either because graphs are labeled clearly or you’ve written additional text to describe how you interpret the output.